EVALUATION OF THE SWAT MODEL’S HYDROLOGY COMPONENT IN THE PIEDMONT PHYSIOGRAPHIC REGION OF MARYLAND

Continuous water quality monitoring is expensive and spatially impractical in mixed land use watersheds. Mathematical watershed-scale models are among the best tools available for analyzing water resources (quantity and quality) issues in spatially diverse watersheds. Although existing watershed-scale models provide some reasonable guidelines, their application without proper validation has resulted in some misconceptions about such models. This study used six years of hydrologic data to calibrate and validate the capability of the SWAT (Soil and Water Assessment Tool) model in predicting surface and subsurface flow for a 340 ha watershed in the Piedmont physiographic region of Maryland. Previous studies have indicated that most existing models only handle subsurface flow bounded by the surface topography, thus neglecting the possible subsurface flow contribution from the outside of watershed, which appears to be a great model deficiency considering the major pathway of pollutant loadings via subsurface flow. Preliminary simulations showed that SWAT underestimated subsurface flow and total streamflow, especially during wet periods. A water budget analysis, therefore, was performed to quantify various components of the hydrologic cycle within the watershed. The resulting imbalance in water budget analysis suggested a considerable groundwater contribution from outside the watershed, especially during wet years. Adjustments to measured base flow and streamflow were made to exclude the extra groundwater recharge from outside the watershed, thus comparing the model predictions with appropriate measured data. However, SWAT seemed to be unable to simulate the extremely wet hydrologic conditions, even after adjustments to measured data. Overall, the hydrology component of the SWAT model is able to perform an acceptable prediction of long-term simulations for management purposes, but fails to have reasonable predictions for short time intervals (i.e., daily).